pixray VS stable-diffusion

Not sure I ran https://github.com/pixray/pixray, which I think is based off of VQGAN. It would fluctuate from 50-100 -> 300-400 watts every few seconds and my desk lamp on the same power strip would flicker dim every time it hit. I game all the time at that wattage, so I assume it was spiking well over 400 when it would kick up.

I used code from https://github.com/pixray/pixray. It is not easy to use directly, and you need datacenter GPU to use pixray well. I ended up maintaining the texts at google sheets and running the model training at GCP Vertex AI infrastructure. I might open source the steps to do that later, but now the code is messy. Pixray does have an accessible web UI at https://replicate.com/pixray/text2image if you are interested to try it out. You can't control all parameters there though. The most recent famous AI art generator is https://openai.com/dall-e-2/, which has a wait list.

This is a just-for-fun post. Over on the discord, Mylie and I have been getting heavily into GAN/CLIP style AI image generation, applied to pixel art with the pixeldrawer module of pixray. It has proven to be extremely fun to input Ultica art as a style guide and then use text prompts to generate new images. We've developed a new method for making seamless tiling pixel art, mixing the AI stuff with our own! Check these out:

This is also hardware-hungry I'm afraid, this uses Pixray here: https://github.com/pixray/pixray

Thanks! I'm using Pixray: https://github.com/pixray/pixray

Can you expand? This looks like something generated by pixray (https://github.com/pixray/pixray), is that what you tweaked? Looks really cool.

They are! And you will be able to generate an unlimited number of them in my game. It is based off this implementation. Hope that helps :)

Stable Diffusion: It is based on a kind of diffusion model called a latent diffusion model, which is trained to remove noise from images in an iterative process. It is one of the first text-to-image models that can run on consumer hardware and has its code and model weights publicly available.

Which is a 1-click install of Stable Diffusion with an alternative web interface. You can choose a different approach but this one is pretty simple and I am new to this stuff.

an invisible watermarking of the outputs, to help viewers identify the images as machine-generated.

It sounds like you just want to set up Stable Diffusion to run locally. I don't think your computer's specs will be able to do it. You need a graphics card with a decent amount of VRAM. Stable diffusion is in Python as is almost every AI open source project I've seen. If you can get your hands on a system with an Nvidia RTX card with as much VRAM as possible, you're in business. I have an RTX 3060 with 12 gigs of VRAM and I can run stable diffusion and a whole variety of open source LLMs as well as other projects like face swap, Roop, tortoise TTS, sadtalker, etc...

Use specific GPU on multi GPU systems · Issue #87 · CompVis/stable-diffusion · GitHub

I dealt with this in one of my posts. At least SD 1.1 till 1.5 are all trained on a batch size of 2048. The version pretty much everyone uses (1.5) is first pretrained at a resolution of 256x256 for 237K steps on laion2B-en, at the end of those training steps it will have seen roughly 500M images in laion2B-en. After that it is pre-trained for 194K steps on laion-high-resolution at a resolution of 512x512, which is a subset of 170M images from laion5B. Finally it is trained for 1.110K steps on LAION aesthetic v2 5+. This is easily verified by taking a glance at the model card of SD 1.5. Though that one doesn't specify for part of the training exactly which aesthetic set was used for part of the training, for that you have to look at the CompVis github repo. Thus at the end of it all both the most recent images and the majority of images will have come from LAION aesthetic v2 5+ (seeing every image approx 4 times). Realistically a lot of the weights obtained from pretraining on 2B will have been lost, and only provided a good starting point for the weights.

stable diffusion · CompVis/stable-diffusion@2ff270f · GitHub